Streaming variational inference for dirichlet process mixtures

Viet Huynh, Dinh Phung, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

3 Citations (Scopus)


Bayesian nonparametric models are theoretically suitable to learn streaming data due to their complexity relaxation to the volume of observed data. However, most of the existing variational inference algorithms are not applicable to streaming applications since they require truncation on variational distributions. In this paper, we present two truncation-free variational algorithms, one for mix-membership inference called TFVB (truncation-free variational Bayes), and the other for hard clustering inference called TFME (truncation-free maximization expectation). With these algorithms, we further developed a streaming learning framework for the popular Dirichlet process mixture (DPM) models. Our experiments demonstrate the usefulness of our framework in both synthetic and real-world data.

Original languageEnglish
Title of host publicationProceedings of Machine Learning Research
Subtitle of host publicationAsian Conference on Machine Learning, 20-22 November 2015, Hong Kong
EditorsGeoffrey Holmes, Tie-Yan Liu
Place of PublicationUSA
PublisherProceedings of Machine Learning Research (PMLR)
Number of pages16
Publication statusPublished - 1 Jan 2015
Externally publishedYes
EventAsian Conference on Machine Learning 2015 - Hong Kong, Hong Kong
Duration: 20 Nov 201522 Nov 2015
Conference number: 7th (Conference website) (Proceedings)

Publication series

NameProceedings of Machine Learning Research
ISSN (Print)1938-7228


ConferenceAsian Conference on Machine Learning 2015
Abbreviated titleACML 2015
CountryHong Kong
CityHong Kong
Internet address

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